Distributed Hierarchical Control for State Estimation with Robotic Sensor Networks

Published

Journal Article

© 2018 IEEE. This paper addresses active state estimation with a team of robotic sensors. The states to be estimated are represented by spatially distributed, uncorrelated, stationary vectors. Given a prior belief on the geographic locations of the states, we cluster the states in moderately sized groups and propose a new hierarchical dynamic programming framework to compute optimal sensing policies for each cluster that mitigates the computational cost of planning optimal policies in the combined belief space. Then, we develop a decentralized assignment algorithm that dynamically allocates clusters to robots based on the precomputed optimal policies at each cluster. The integrated distributed state estimation framework is optimal at the cluster level but also scales very well to large numbers of states and robot sensors. We demonstrate efficiency of the proposed method in both simulations and real-world experiments using stereoscopic vision sensors.

Full Text

Duke Authors

Cited Authors

  • Freundlich, C; Zhang, Y; Zavlanos, MM

Published Date

  • December 1, 2018

Published In

Volume / Issue

  • 5 / 4

Start / End Page

  • 2023 - 2035

Electronic International Standard Serial Number (EISSN)

  • 2325-5870

Digital Object Identifier (DOI)

  • 10.1109/TCNS.2017.2782481

Citation Source

  • Scopus